A deep dive into an accelerometer dataset for understanding human movement, activity recognition, and physical behavior analysis.

The accelerometer case study dataset refers to a collection of data generated by accelerometers, which are sensors commonly used to measure and record acceleration forces. This dataset is specifically designed for studying human motion and activity patterns.

The dataset captures various physical activities and movements performed by individuals while wearing accelerometer devices. It records acceleration data in multiple axes, such as x, y, and z, providing a detailed representation of the forces experienced during different activities.

Researchers and data scientists can utilize the accelerometer case study dataset to analyze and understand human motion and activity recognition. By examining the dataset, they can explore patterns, correlations, and trends related to specific activities, such as walking, running, sitting, or even more complex movements like jumping or cycling.

The accelerometer dataset serves as a valuable resource for developing and evaluating algorithms and models for activity recognition, gait analysis, fitness tracking, and other applications in the fields of healthcare, sports science, and wearable technology. Researchers can also use this dataset to investigate the impact of various factors, such as age, gender, or environmental conditions, on human movement patterns.

By leveraging the accelerometer case study dataset, researchers can gain insights into human behavior, identify abnormal movement patterns, monitor physical activity levels, and design personalized interventions for promoting healthy lifestyles. Additionally, the dataset can aid in the development of innovative solutions for activity tracking, fall detection, and other applications aimed at improving human well-being and quality of life.

Discover the booming market for markerless 3D movement analysis systems. This in-depth analysis reveals a CAGR of 11.6%, driven by sports training, rehabilitation, and technological advancements. Explore market size, segmentation, key players, and future growth projections.

This is an initial study to characterise rollator movement. An inertial measurement unit (IMU) was used to measure the motion of the rollator and analytical approaches were developed to extract features characterising the rollator movement, properties of the surface, and push events. The analytics were tested in two situations, firstly a healthy participant used a rollator in a laboratory using a motion capture system to obtain ground truth. Secondly the IMU was used to measure the movement of a rollator being used by a user with multiple sclerosis (MS) on a flat surface, cross-slope, up and down slopes, and up and down a step The dataset of inertial measurement unit is comprised of seven straight-lighting walking trials (between 4-5 m) performed by a healthy participant using a rollator in a gait lab. The raw data were in txt format recorded by Xsens Swinda 2 and subsequently analysed using a bespoke MATLAB script to calculate the distance travelled by a rollator.

This multi-city human mobility dataset contains data from 4 metropolitan areas (cities A, B, C, D), somewhere in Japan. Each city is divided into 500 meters x 500 meters cells, which span a 200 x 200 grid. The human mobility datasets contain the movement of individuals across a 75-day period, discretized into 30-minute intervals and 500-meter grid cells. Each city contains the movement data of 100,000, 25,000, 20,000, and 6,000 individuals, respectively.

While the name or location of the city is not disclosed, the participants are provided with points-of-interest (POIs; e.g., restaurants, parks) data for each grid cell (~85 dimensional vector) for the four cities as supplementary information (e.g., POIdata_cityA). The list of 85 POI categories can be found in POI_datacategories.csv.

This dataset was used for the HuMob Data Challenge 2024 competition. For more details, see https://wp.nyu.edu/humobchallenge2024/

Researchers may use this dataset for publications and reports, as long as: 1) Users shall not carry out activities that involve unethical usage of the data, including attempts at re-identifying data subjects, harming individuals, or damaging companies, and 2) The Data Descriptor paper of an earlier version of the dataset (citation below) needs to be cited when using the data for research and/or commercial purposes. Downloading this dataset implies agreement with the above two conditions.

Yabe, T., Tsubouchi, K., Shimizu, T., Sekimoto, Y., Sezaki, K., Moro, E., & Pentland, A. (2024). YJMob100K: City-scale and longitudinal dataset of anonymized human mobility trajectories. Scientific Data, 11(1), 397. https://www.nature.com/articles/s41597-024-03237-9

The markerless 3D movement analysis systems market is experiencing robust growth, driven by increasing demand across diverse sectors like healthcare, sports, and entertainment. The market size in 2025 is estimated at $624 million. While the CAGR is not provided, considering the rapid technological advancements in computer vision and AI, coupled with the growing adoption of sophisticated motion capture techniques, a conservative estimate of the CAGR for the forecast period (2025-2033) would be around 15%. This implies significant market expansion, potentially reaching over $2 billion by 2033. This growth is fueled by several key factors. The non-invasive nature of markerless systems eliminates the complexities and time constraints associated with traditional marker-based systems, making them more accessible and user-friendly. Furthermore, advancements in AI and machine learning algorithms are enhancing the accuracy and efficiency of movement analysis, leading to more reliable and insightful data for researchers and practitioners. The rising prevalence of chronic diseases and the need for personalized rehabilitation programs in healthcare are further driving market adoption. The increasing use of motion capture in sports for performance analysis and injury prevention also contributes to market growth. However, certain challenges remain. High initial investment costs for advanced systems and the need for specialized expertise to operate and interpret the data can limit widespread adoption, particularly among smaller businesses or individual practitioners. Moreover, data privacy and security concerns related to the collection and storage of sensitive movement data require careful consideration and robust security protocols. Despite these restraints, the overall market outlook for markerless 3D movement analysis systems remains highly positive, with continued innovation and expanding applications across various sectors poised to drive substantial growth in the coming years. The competitive landscape is dynamic, with several established players and emerging companies vying for market share. Companies like Vicon, Qualisys, and others are leading the charge in innovation and market penetration.

Leverage the most reliable and compliant mobile device location/foot traffic dataset on the market. Veraset Movement (Mobile Device GPS / Foot Traffic Data) offers unparalleled insights into footfall traffic patterns across North America.

Covering the United States, Canada and Mexico, Veraset's Mobile Location Data draws on raw GPS data from tier-1 apps, SDKs, and aggregators of mobile devices to provide customers with accurate, up-to-the-minute information on human movement. Ideal for ad tech, planning, retail analysis, and transportation logistics, Veraset's Movement data helps in shaping strategy and making data-driven decisions.

Veraset’s North American Movement Panel: - United States: 768M Devices, 70B+ Pings - Canada: 55M+ Devices, 9B+ Pings - Mexico: 125M+ Devices, 14B+ Pings - MAU/Devices and Monthly Pings

Uses for Veraset's Mobile Location Data: - Advertising - Ad Placement, Attribution, and Segmentation - Audience Creation/Building - Dynamic Ad Targeting - Infrastructure Plans - Route Optimization - Public Transit Optimization - Credit Card Loyalty - Competitive Analysis - Risk assessment, Underwriting, and Policy Personalization - Enrichment of Existing Datasets - Trade Area Analysis - Predictive Analytics and Trend Forecasting

Mean absolute error (MAE) in kinematics and kinetics from OpenCap compared to laboratory-based motion capture and force plates.

The global biomechanical analysis software market is experiencing robust growth, driven by the increasing adoption of advanced motion capture technologies in various sectors, including healthcare, sports science, and ergonomics. The market's expansion is fueled by the rising demand for precise and objective biomechanical assessments to improve athletic performance, enhance rehabilitation strategies, and design safer and more ergonomic products. The integration of artificial intelligence (AI) and machine learning (ML) algorithms is further accelerating market growth, enabling automated data analysis and the generation of more insightful reports. Cloud-based solutions are gaining traction due to their accessibility, scalability, and cost-effectiveness compared to locally deployed systems. The medical sector represents a significant market segment, with applications ranging from gait analysis for patients with musculoskeletal disorders to surgical planning and prosthetic design. Scientific research also constitutes a substantial portion of the market, as researchers utilize biomechanical analysis software to investigate human movement patterns and develop new interventions. While the market presents significant opportunities, several factors are influencing its growth trajectory. Competition among established players and emerging startups is intense, leading to price pressures and a continuous drive for innovation. The high cost of advanced motion capture systems and software licenses can limit adoption, especially in resource-constrained settings. Moreover, the need for specialized expertise in biomechanics and data interpretation can pose a barrier to widespread adoption. Despite these challenges, the long-term outlook for the biomechanical analysis software market remains positive, driven by continuous technological advancements, expanding applications, and increasing awareness of the benefits of biomechanical analysis in various domains. We project a steady CAGR, reflecting the gradual but consistent market expansion and technological advancements within the sector.

Welcome 🙋‍♀️

Introducing our benchmark for analyzing human movement, created for researchers and machine learning enthusiasts seeking actual motion capture data from industrial operators and expert craftsmen. Our benchmark comprises seven unique datasets, each containing inertial-based motion capture data that can be used for a wide range of applications, including dexterity analysis, simulation, and virtual animation. Additionally, we provide an additional dataset designed for ergonomic analysis of human movements. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2168390%2F2eed1cbbce9a4cecbe07fd2201db636c%2FCapture%20dcran%202023-04-19%20171927.png?generation=1689158042295175&alt=media" alt="">

Datasets description

On the benchmark's dedicated website (AImove Benchmark), you will find detailed information about the motion capture system used and the recorded movements for each dataset.

Citation

When utilizing our datasets, kindly remember to acknowledge and cite our contributions. We greatly appreciate shoutouts! For accessing our papers, please refer to the following link: Olivas-Padilla2023

The global biomechanics tracking system market is experiencing robust growth, driven by increasing demand across diverse sectors like sports medicine, healthcare, and ergonomics. The market's expansion is fueled by advancements in sensor technology, miniaturization, and the development of sophisticated software for data analysis. This allows for more precise and detailed motion capture, enabling clinicians and researchers to better understand human movement and develop targeted interventions. The rising prevalence of musculoskeletal disorders and the growing need for objective assessment tools in rehabilitation are also key factors contributing to market expansion. Furthermore, the integration of biomechanics tracking systems with other technologies, such as virtual reality and artificial intelligence, is opening new avenues for applications in areas such as gait analysis, virtual surgery planning, and personalized fitness training. We estimate the market size in 2025 to be approximately $750 million, based on industry reports showing a similar-sized market for related motion capture technologies and considering a projected CAGR of around 8% for the forecast period. Competitive rivalry within the biomechanics tracking system market is intense, with a mix of established players and emerging companies vying for market share. Key players like Vicon, Motion Analysis, and Qualisys hold significant positions, leveraging their established brand reputation and extensive product portfolios. However, smaller, more agile companies are making inroads with innovative solutions, focusing on areas like markerless motion capture and improved data processing capabilities. The market is segmented by technology (optical, inertial, electromagnetic), application (sports science, healthcare, ergonomics), and end-user (research institutions, hospitals, sports teams). Regional growth is expected to be particularly strong in North America and Europe, owing to the high adoption rates of advanced technologies and well-established healthcare infrastructure. However, Asia-Pacific is projected to exhibit rapid growth driven by increasing healthcare expenditure and the rising prevalence of chronic diseases. Continued technological innovation and the development of more user-friendly and affordable systems will shape future market dynamics.

Leverage the most reliable and compliant global mobility and foot traffic dataset on the market. Veraset Movement (Mobile Device GPS Mobility Data) offers unparalleled real-time insights into footfall traffic patterns globally.

Covering 200+ countries, Veraset's Mobile Location Data draws on raw GPS data from tier-1 apps, SDKs, and aggregators of mobile devices to provide customers with accurate, up-to-the-minute information on human movement.

Ideal for ad tech, planning, retail analysis, and transportation logistics, Veraset's mobile location data helps in shaping strategy and making data-driven decisions.

Veraset Global Movement panel (mobile location) includes: - 1.8+ Billion Devices Monthly - 200 Billion Pings Monthly Device and Ping counts by Country are available upon request

Common Use Cases of Veraset's Mobile Location Data: - Advertising - Ad Placement, Attribution, and Segmentation - Audience Creation/Building - Dynamic Ad Targeting - Infrastructure Plans - Route Optimization - Public Transit Optimization - Credit Card Loyalty - Competitive Analysis - Risk assessment, Underwriting, and Policy Personalization - Enrichment of Existing Datasets - Trade Area Analysis - Predictive Analytics and Trend Forecasting

Please visit: https://www.veraset.com/docs/movement for more information and schemas

The mean absolute error (MAE) and root mean square error (RMSE) are shown for each degree of freedom, activity, camera combination, and pose detection algorithm. (XLSX)

Unacast Mobility Data enables privacy-friendly analysis of human movement at a local or global level.

Organizations use mobility data to answer key questions, including:

-How does human mobility in one area compare to another? -When exactly did changes in foot traffic occur? -How does the activity between locations change over time? -Where do visitors travel from to visit places of interest?

Mobility data can be used in many different ways to understand changes in human movement across different areas, predict future human mobility trends, and inform operational and marketing strategies.

Learn more at https://www.unacast.com.

These files provide summary statistics (mean, SD, Max) for counts/minute of the accelerometry data. Each line represents summarizes the data of one participant. There are separate files for wrist and waist, as well day (6a-10p), night and combined.

Leverage the most reliable and compliant mobile device location/foot traffic dataset on the market.

Veraset Movement (Mobile Location Data) offers unparalleled insights into footfall traffic patterns across dozens of European countries.

Covering 45+ European countries, Veraset's Mobile Location Data draws on raw GPS data from tier-1 apps, SDKs, and aggregators of mobile devices to provide customers with accurate, up-to-the-minute information on human movement. Ideal for ad tech, planning, retail, and transportation logistics, Veraset's Movement data helps shape strategy and make impactful data-driven decisions.

Veraset’s European Movement Panel includes the following countries: - United Kingdom-GB - Germany-DE - France-FR - Spain-ES - Italy-IT - The Netherlands-NL - Switzerland-CH - Belgium-BE - Sweden-SE - Austria-AT - Denmark-DK - Finland-FI - Cyprus-CY - Poland-PL - Ireland-IE - Portugal-PT - Romania-RO - Hungary-HU - Czech Republic-CZ - Greece-GR - Bulgaria-BG - Lithuania-LT - Croatia-HR - Norway-NO - Latvia-LV - Luxembourg-LU - Slovakia-SK - Estonia-EE - Cayman Islands-KY - Slovenia-SI - Vatican city-VA - Turks and Caicos Islands-TC - Bermuda-BM - Malta-MT - Iceland-IS - Liechtenstein-LI - Monaco-MC - British Virgin Islands-VG - Anguilla-AI - Andorra-AD - Greenland-GL - San Marino-SM - Federated States of Micronesia-FM - Montserrat-MS - Pitcairn islands-PN

Common Use Cases of Veraset's Mobile Location Data: - Advertising - Ad Placement, Attribution, and Segmentation - Audience Creation/Building - Dynamic Ad Targeting - Infrastructure Plans - Route Optimization - Public Transit Optimization - Credit Card Loyalty - Competitive Analysis - Risk assessment, Underwriting, and Policy Personalization - Enrichment of Existing Datasets - Trade Area Analysis - Predictive Analytics and Trend Forecasting

This dataset contains information on member participants including date of birth, gender, postcode, as well as coach and official data (gender, postcode, level etc). The data is collected routinely by State Sporting Associations and shared with the Sport and Recreation Spatial project. The period will depend on systems of the sports but generally 2010, 2011, 2012.

The dataset portal is in development and expected to be available mid 2013. The datasets are available on request. Project Organization Unit: Institute for Sport, Exercise and Active Living, Victoria University

This repository contains anonymised data supporting the article “Movement fluency metrics during multi-phase sit-to-walk and reach-to-grasp: test–retest reliability and agreement between laboratory-based and portable 3D motion analysis systems.” The dataset quantifies movement fluency during two functional tasks and evaluates repeatability across sessions and agreement between laboratory and portable 3D motion analysis systems.

Contents The dataset includes the information required to reproduce the fluency metrics reported in the article. Both Vicon data and Biokido data folders contain data within participant folders. The protocol was for participants to have these metric collected on two separate occasions, data is contained in subfolders S1 for Session 1 and S2 for session 2.

Vicon data MATLAB structures sampled at 100 Hz. Only markers and outputs needed for analyses are included.

Reach-to-grasp (RTG)

Wrist trajectories (LWRA, LWRB, RWRA, RWRB) for smoothness (SPARC).

Trigger channel and trial onset.

Participant ID and trial name.

Trial naming standardised to task conditions:

RTG-MD = dominant hand to midline

RTG-MND = non-dominant hand to midline

RTG-CD = dominant hand to contralateral

RTG-CND = non-dominant hand to contralateral

Repetitions appended (e.g. RTG-MD1). Original trial code preserved as OriginalTrial.

Sit-to-walk (STW)

TRX (thorax, 3D) for smoothness (SPARC).

Gait events: LHS, LTO, RHS, RTO.

Participant ID and trial name.

Common

Sampling: 100 Hz.

Global coordinate system info.

Biokido data Trimmed CSV files sampled at 90 Hz. Each file is a single RTG or STW trial containing required 3D trajectories.

RTG CSVs

tX,tY,tZ: thorax (mm).

wX,wY,wZ: wrist (mm).

frame_number: sample index.

time_stamp: acquisition time.

STW CSVs

X,Y,Z: thorax (mm).

XF,YF,ZF: filtered thorax.

Y1,Z1,Y2,Z2,Y3,Z3: derived components.

Y1F,Z1F,Y3F,Z3F: filtered counterparts.

frame_number, time_stamp.

“F” = filtered version.

Organisation CSV files are stored under Biokido files/RTG and Biokido files/STW with participant subfolders (e.g. PH001). Trials named by task condition (RTG-MD, RTG-MND, RTG-CD, RTG-CND, STW1, STW2…).

Intended use Enables reproduction of reliability and agreement analyses and supports further work on movement fluency, phase-specific behaviour, and system comparisons.

Ethics All participants gave informed consent. Data are fully anonymised.

Publication Please cite both this dataset and the article when using these data.

Data used to determine optimal metabolic cost when manipulating ground contact time and keeping stride frequency and running velocity constant. Ground contact time is in seconds. VO2 (oxygen consumption) is in ml per kg per minute. Self-selected is binary data with one represented the self-selected ground contact time and zero representing the manipulating ground contact times.

The data was originally collected by Australian Sports Commission (ASC) and State Government departments. 2001-2010. Data includes type of sports and recreation played, age, sex, setting of participation, and frequency of participation. The data covers 200,000 survey participants. This dataset has been cleaned.

The dataset portal is in development and expected to be available mid 2013. The datasets are available on request. Project Organization Unit: Institute for Sport, Exercise and Active Living, Victoria University

Data and code for De Graaf et al. (2019): Influence of Arm Swing on Cost of Transport during Walking (https://doi.org/10.1101/426775).

The ArmSwingData folder contains two subfolders. One with the raw data and one with the necessary scripts to run the analyses. The analysis can be run from the Main.m script located in the top folder. Start.m initializes the program, and saves some important variables in infovar.mat so most programs can be ran separately, without being called from Main.m. If you change the name of the ArmSwingData folder, make sure to change the name for the directory in this script.

Data is ordered in the subfolder ‘Data’. For each participant (PP01, PP02, …, PP12) three folders exist:

Cosmed: contains excel files with the metabolic (cosmed) data, as well as age/height/weight information of the participant

ForcePlate: contains .afp and .afp2 files with kinetic data for forceplate 1 and 2 respectively. You can find which file corresponds to what condition in the file ‘Main_Forceplate.m’ stored in the software subfolder. Heelstrike information for the forceplate data has already been stored in the variable PP**_HS.mat, but can also be recalculated and checked from within the software. To do this open ‘Main_Forceplate.m’ and uncomment line 23 that runs ‘Forceplate_HeelStrike.m’ .

Xsens: contains .mvnx for all conditions. You can find which file corresponds to what condition in the file ‘Main_Xsens.m’ stored in the software subfolder. Heelstrike information for the xsens data has already been stored in the variable PPXX_HS.mat, but can also be recalculated and checked from within the software. To do this open ‘Main_Xsens.m’ and uncomment line 23 that runs ‘Xsens_HeelStrike.m’ .

All scripts (except for start-up files Main.m and Start.m) are located in the software folder:

=VU 3D Model=: the somewhat under documented VU 3D model, several functions of which are being used;

Cosmed: All scripts belonging to the cosmed analysis. These can be all run from Main_Cosmed.m

ForcePlate: All scripts belonging to the kinetic analysis. These can be all run from Main_ForcePlate.m

Xsens: All scripts belonging to the kinematic analysis. These can be all run from Main_Xsens.m

Plotting: All scripts necessary for creating the figures featured. These can be all run from Plots.m

All the Main_*.m files can in turn be ran from Main.m. The Main_*.m files also contain brief descriptions of the files. All files first check if the product they create already exist. If so they will not run. If you want to run them anyways, simply inverse the if ~exist statement by removing the tilde, the program will then be executed and the existing product will be overwritten.

N.B. For Xsens_Align, checking if it’s product exists takes a long time, so it could be wise to not run this program again after the first time (set run=false at the start of the program, or simply comment the line in Main_Xsens)